{
 "cells": [
  {
   "cell_type": "raw",
   "id": "07dfd549",
   "metadata": {},
   "source": [
    "实验介绍\n",
    "    该实验数据集记录了鱼市销售中7种常见的不同鱼种。有了这个数据集，可以使用机器学习回归算法预测鱼的重量。\n",
    "    预测重量，相当于预测一个具体的值，这就是回归问题了\n",
    "实验目的\n",
    "    具备使用 Python 进行行业数据分析的能力，能够使用所学的数据挖掘知识对实际回归问题进行分析和处理。\n",
    "预备知识\n",
    "        Python 的基本操作和 Pandas 的数据分析和处理的基本操作;数据挖掘回归算法原理，及对应的模型评估值指标(包括指标参数的意义)，能够\n",
    "        使用sklearn 中相关算法进行建模分析;了解 Python 数据可视化的常用方法和库。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "625702a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dcf31d4",
   "metadata": {},
   "source": [
    "1.读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "810623b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bream</td>\n",
       "      <td>242.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>25.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.5200</td>\n",
       "      <td>4.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bream</td>\n",
       "      <td>290.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>31.2</td>\n",
       "      <td>12.4800</td>\n",
       "      <td>4.3056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bream</td>\n",
       "      <td>340.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>26.5</td>\n",
       "      <td>31.1</td>\n",
       "      <td>12.3778</td>\n",
       "      <td>4.6961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bream</td>\n",
       "      <td>363.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>12.7300</td>\n",
       "      <td>4.4555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bream</td>\n",
       "      <td>430.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>12.4440</td>\n",
       "      <td>5.1340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Species  Weight  Length1  Length2  Length3   Height   Width\n",
       "0   Bream   242.0     23.2     25.4     30.0  11.5200  4.0200\n",
       "1   Bream   290.0     24.0     26.3     31.2  12.4800  4.3056\n",
       "2   Bream   340.0     23.9     26.5     31.1  12.3778  4.6961\n",
       "3   Bream   363.0     26.3     29.0     33.5  12.7300  4.4555\n",
       "4   Bream   430.0     26.5     29.0     34.0  12.4440  5.1340"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./Fish.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a6d945",
   "metadata": {},
   "source": [
    "2.数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a742af6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bream', 'Roach', 'Whitefish', 'Parkki', 'Perch', 'Pike', 'Smelt'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看鱼的种类\n",
    "df['Species'].unique()    #unique：用来去重一列数据  ，这里查看鱼的种类有多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "799543d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Perch        56\n",
       "Bream        35\n",
       "Roach        20\n",
       "Pike         17\n",
       "Smelt        14\n",
       "Parkki       11\n",
       "Whitefish     6\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].value_counts()  #value_counts(): 也有去重的效果，这里查看鱼的种类有多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2a4630a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 159 entries, 0 to 158\n",
      "Data columns (total 7 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   Species  159 non-null    object \n",
      " 1   Weight   159 non-null    float64\n",
      " 2   Length1  159 non-null    float64\n",
      " 3   Length2  159 non-null    float64\n",
      " 4   Length3  159 non-null    float64\n",
      " 5   Height   159 non-null    float64\n",
      " 6   Width    159 non-null    float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 8.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()   #查看df每列的数据类型，观察数据Species  这列非数值类型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89d45bea",
   "metadata": {},
   "source": [
    "3.特征编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39a5a8ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Species这列非数值类型,作特征编码，使用label-encoding进行编码\n",
    "\n",
    "# df['Species Code'] = df['Species'].astype('category').cat.codes   #这里实验教材pdf中是新增了一列来保存转换的值  category：分类的意思，这\n",
    "# 种也要掌握一下，怕出填空题\n",
    "df['Species'] = pd.factorize(df['Species'])[0]    #还是用我们之前的方式吧，把Species列的值用0,1,2，...来替换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e1bf9d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3,\n",
       "        3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5,\n",
       "        5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
       "        6, 6, 6, 6, 6], dtype=int64),\n",
       " Int64Index([0, 1, 2, 3, 4, 5, 6], dtype='int64'))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.factorize(df['Species'])  #输出的是一个元祖，我们只要数据那一个，也就是array，所以上面取下标为0的"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d39f0fe",
   "metadata": {},
   "source": [
    "4.缺失值的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87ac5a7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Species    0\n",
       "Weight     0\n",
       "Length1    0\n",
       "Length2    0\n",
       "Length3    0\n",
       "Height     0\n",
       "Width      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.isnull().any()   # 也是查缺失值的，\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f17a4cf5",
   "metadata": {},
   "source": [
    "5.绘制其他属性和鱼的重量之间的分布关系散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a0ad7783",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 方式一：先用matplotlib下的pyplot去绘制图\n",
    "import matplotlib.pyplot as plt\n",
    "list_col  = [col for col in df.columns if col !='Weight'] \n",
    "for x in list_col:\n",
    "    plt.scatter(x=df[x],y=df['Weight'],c='green')   # scatter：分散，也就是创建散点图  c:指定颜色\n",
    "    plt.xlabel(x)\n",
    "    plt.ylabel('Weight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e8824b82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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q6h3vm94rU3gwq6COEFVXV+vqq6/WihUrXJ5/4okn9OSTT2rFihXat2+fUlJSdP311+vs2bOONgsWLNCbb76p9evXa/v27aqqqtKkSZPU0NDgaDNjxgzl5eVp48aN2rhxo/Ly8jRz5ky/3x+A8BCsaauMuOg2z39yqrLDfXB1b02DIcn5XpnCg1kFdYRo4sSJmjhxostzhmHo6aef1i9/+UtNnTpVkvTSSy8pOTlZr732mn7yk5/IZrNp1apVevnllzV+/HhJ0iuvvKL09HS9//77uuGGG/Tpp59q48aN2r17t4YMGSJJevHFFzVs2DAdOXJEl19+ucvfX1tbq9raWsf7yspKX946gBBhn7Zqrum0lb9WfV2W2EMjsxK1/WipGo2W5xulDvWhtXtrzn6v2z4rDdqzAIItZHOICgsLVVxcrAkTJjiORUVFadSoUdq5c6ckaf/+/aqvr3dqk5aWpuzsbEebXbt2yWq1OoIhSRo6dKisVqujjStLly6V1Wp1vNLT0319iwBCgLtpq+Pl/s2hWT49R/3SYvzSB3f31tzBkxV+6QcQDkI2ICouLpYkJScnOx1PTk52nCsuLla3bt0UGxvbZpukpKQWn5+UlORo48r9998vm83meJ08ebJD9wMgNLmbtro03r8jItboSP3p9hy/9MHdvTWXkx7b5nl/PwsgmEI2ILKzWCxO7w3DaHGsueZtXLV39zlRUVGKiYlxegHofOzTVhHN/j2IsFg0MisxIFNE3/Sh5bnY6EjFRXfr4Oe2/W+m/V5H9k0M+rMAgiVkA6KUlBRJajGKU1JS4hg1SklJUV1dnSoqKtps8+WXX7b4/NLS0hajTwDMafn0HI3ok+B0bESfBC2f3vbIja/7EHNhy1Vctpr6DiU0u7q32GarxZreayg8CyAYQrZSdWZmplJSUrR582bl5Jz/i1hXV6fc3Fz97ne/kyRdc801ioyM1ObNmzVt2jRJUlFRkQ4fPqwnnnhCkjRs2DDZbDbt3btXgwcPliTt2bNHNptNw4cPD8KdAQg11uhIrZ0zWIVl1TpeXh2U7TPKq2tbrP6SOp5Y3dq9tXavofAsgGAIakBUVVWlo0ePOt4XFhYqLy9PcXFxuuSSS7RgwQI99thjysrKUlZWlh577DFFR0drxowZkiSr1ao5c+Zo0aJFio+PV1xcnBYvXqwBAwY4Vp1deeWVuvHGGzV37lw9//zzkqQf//jHmjRpUqsrzACYU2aC91/+vtqDzJPk7o58fvN7c3ev7XkWQDgLakD04YcfasyYMY73CxculCTNnj1ba9as0X333adz587pZz/7maMw46ZNm9SzZ0/HNU899ZS6du2qadOmOQozrlmzRhEREY42r776qubPn+9YjTZ58uRWax8BgCd8XcDQXQJ0hMWiLUdKGLEJADbaNSeLYRguql+gucrKSlmtVtlsNhKsAWjWqr3acbTMaduNCItFI/okaO2cwT77zC46P43VWmVp+A5VujsnT7+/QzapGgBClb/2IHOV0GyNjlTludYrS8N3qNJtbiGbVA0Aocpf+T7NE5ojLBbN+sveFu2oHO17waxYjtDACBEAeMnfxRwzE7przOVJLUagmqNytO8Eu2I5go+ACAC8FKhijsGuom0mPGsQEAFAO7SngGFBaZW2HCnxOMcoFKpomwXPGqwy8xCrzAC4su2zEh08eUYDL4nVt7MSXbbpyOole6VqVj75H8+6c/L0+5uAyEMERACa8ibI8cUSfSpHBw7PunPx27L7iIgIlZSUtDheXl7uVAwRADozT5do+2qJvj3Rmi9o/+NZm5PXAVFrA0q1tbXq1q19OzIDQDjxJshh9RIQHjyuQ/SnP/1JkmSxWPTnP/9ZPXr0cJxraGjQtm3bdMUVV/i+hwAQYrypQ+Tp6iW2iwCCy+OA6KmnnpJ0foToueeec5oe69atmy699FI999xzvu8hAIQYb5Zo21cvNc8hsntgwyFZLNLOY+WOYyTyAoHndVL1mDFjtGHDBsXGxvqrTyGJpGoATXmTKO1q9VJbOronGoBv+C2pesuWLaYLhgCgOW/qEFmjI7Vkcj+PP7uje6IB8J7Xe5k1NDRozZo1+uc//6mSkhI1NjY6nf/ggw981jkACFXN9x1zl/vjLu/IlfbuiQbAe14HRPfee6/WrFmjm266SdnZ2bI0q+oJAGaSmeBZErS7vCNX2C4CCByvA6L169frjTfe0He+8x1/9AcAOiV3ydVN2XOIGB0CAsfrHKJu3bqpT58+/ugLAHRqrvKOhl0Wr+G9452OudsTDYDveb3KbNmyZSooKNCKFStMNV3GKjMAvuIq74jtIgD/8OleZlOnTnV6/8EHHyguLk79+/dXZKRznYwNGza0s8uhjYAIAIDw4+n3t0c5RFar1en9rbfe2rHeAUAQhVpV6FDrD2BGHgVEq1ev9nc/AMDvvNmh3oz9AczM66RqAAhHBaVV+v6f92h7s2rRrnaoD5T56/K042hZyPQHMDOvl93n5OS4TKa2WCy64IIL1KdPH915550aM2aMTzoIAB3hahSmqaZVoQM5XVVQWuWyT8HqD2B2Xo8Q3XjjjSooKFD37t01ZswYjR49Wj169NCxY8d07bXXqqioSOPHj9ff/vY3f/QXALziahTGlePlgd0mw13l6kD3BzA7r0eIysrKtGjRIj344INOxx955BGdOHFCmzZt0kMPPaTf/va3uuWWW3zWUQDwVmujMK4Euiq0u8rVVKkGAsvrEaI33nhD06dPb3H89ttv1xtvvCFJmj59uo4cOdLx3gFAB3iyf1iExaKRWYlBmZ7KvihGXZplIHSRgtYfwMy8HiG64IILtHPnzhbVqnfu3KkLLrhAktTY2KioqCjf9BAA2smT/cMCXRXaXU5To6SvGxtlq6lnpRkQQF4HRPPmzdNdd92l/fv369prr5XFYtHevXv15z//WQ888IAk6b333lNODmXnAQRXa/uHdbFI/dJitHz6wICPxHiS07Sn4LTmrTuotXMGB6hXALzeukOSXn31Va1YscIxLXb55Zdr3rx5mjFjhiTp3LlzjlVnnQWVqoHwZKup17x1B0Oi1k9BaZXGLsv1uP2WxaOZOgM6yKeVqpu74447dMcdd7R6/sILL2zPxwKAz1mjI7V2zmCv9grzV+VoT3KamjpeztJ7IFDaFRABQLjJTHAf3Pi7crQnOU1NsdIMCByPVpnFxcWprOz8nHdsbKzi4uJafQFAuPJ35Wh7TlOEi+K2TQVz5RtgVh6NED311FPq2bOnJOnpp5/2Z38AICgCVTl6+fScFjlNsdGRqqipd7wP9Mo3AB4GRLNnz3b5MwB0Fp5UjvZFQNRaTpM3OU4AfK9dOUTHjh3T6tWrdezYMf3xj39UUlKSNm7cqPT0dPXv39/XfQQAvwt05ejmOU2e5DgB8B+vK1Xn5uZqwIAB2rNnjzZs2KCqqipJ0kcffaSHHnrI5x0EgEBoLb+HfJ7gKCit0pYjJSosY083BIbXAdEvfvELPfLII9q8ebO6devmOD5mzBjt2rXLp50DgEBaPj1HI/okOB0jnyewztTUadaqvRq7LFc/WL1PY/6wVbNW7ZWtSY4V4A9eT5kdOnRIr732WovjiYmJKi8v90mnACAY2lOzCL7V1ko/KnfDn7weIerVq5eKiopaHD948KAuuugin3QKAIIpM6G7xlyeRDAUYPaVfg3NNlBoutIP8BevA6IZM2bo5z//uYqLi2WxWNTY2KgdO3Zo8eLFmjVrlj/6CAAwAU9W+gH+4nFAdPToUUnSo48+qoyMDF100UWqqqpSv379NHLkSA0fPly/+tWvfN7BSy+9VBaLpcXr7rvvliTdeeedLc4NHTrU6TNqa2s1b948JSQkqHv37po8ebK++OILn/cVANB+gV7pBzTlcQ5R3759ddFFF2nMmDEaN26cfvOb3+jAgQNqbGxUTk6OsrKy/NLBffv2qaGhwfH+8OHDuv766/W9733PcezGG2/U6tWrHe+bJntL0oIFC/TOO+9o/fr1io+P16JFizRp0iTt379fERERfuk3AMA79pV+O46WOU2bRVgsGtEngSlM+JXHAVFubq5yc3O1detW3XPPPfrqq690ySWXaOzYsaqrq1N0dLRfcogSExOd3j/++OPq3bu3Ro0a5TgWFRWllJQUl9fbbDatWrVKL7/8ssaPHy9JeuWVV5Senq73339fN9xwg8vramtrVVtb63hfWVnZ0VsBALjhqpI3K/0QCBbDaJa95oH6+nrt2rVLW7du1datW7V7927V1taqT58+OnLkiD/6KUmqq6tTWlqaFi5cqAceeEDS+Smzt956S926dVOvXr00atQoPfroo0pKSpIkffDBBxo3bpxOnz6t2NhYx2ddffXVmjJlih5++GGXv2vJkiUuz9lsNsXExPjh7gAAdqz0g69UVlbKarW6/f5uV0Bkd+7cOW3fvl3vvfeeXnzxRVVVVTlNb/naG2+8oRkzZujzzz9XWlqaJOn1119Xjx49lJGRocLCQj344IP6+uuvtX//fkVFRem1117TD37wA6fRHkmaMGGCMjMz9fzzz7v8Xa5GiNLT0wmIgBBXUFqlE6dr+CIFIMnzgMirOkRfffWVdu7cqS1btmjr1q3at2+fMjMzNWrUKK1cudJpGssfVq1apYkTJzqCIUm67bbbHD9nZ2dr0KBBysjI0N///ndNnTq11c8yDEOWNnacjoqKUlRUlG86DsDvztTUaf66PKepluy0GD126wBdld4reB0DEBY8DohGjRqlffv2qXfv3ho5cqTmzZunUaNGKTk52Z/9czhx4oTef/99bdiwoc12qampysjIUH5+viQpJSVFdXV1qqiocJoyKykp0fDhw/3aZwCB46qg3+FTlZr8zA6NzErU8uk5skZHBql3AEKdx8vud+7cqYSEBMcqs7FjxwYsGJKk1atXKykpSTfddFOb7crLy3Xy5EmlpqZKkq655hpFRkZq8+bNjjZFRUU6fPgwARHQSbRW0M9u+9FSzVt30NGWPbIANOfxCNGZM2f0r3/9S1u3btXvfvc7TZ8+XX379tWoUaM0evRojRo1qsWKMF9pbGzU6tWrNXv2bHXt+k2Xq6qqtGTJEn33u99Vamqqjh8/rgceeEAJCQm69dZbJUlWq1Vz5szRokWLFB8fr7i4OC1evFgDBgxwrDoDEN7cFfRrNKRt+aX63nM7te94heM4I0cA7NqdVH327Flt377dkU/073//W1lZWTp8+LCv+6hNmzbphhtu0JEjR9S3b1/H8XPnzmnKlCk6ePCgzpw5o9TUVI0ZM0a//e1vlZ6e7mj31Vdf6f/8n/+j1157TefOndO4ceP07LPPOrVxx9OkLAC+lXukRHlfnNHAS2L17SzX/9NVUFqlscty3X5WF8v54MjOXt+mtT2yXCVok7QNhBe/rzJrbGzUvn37tGXLFm3ZskXbt2/XV1995ddVZsFEQAQE1onyak15ZocqmuxyHhsdqbfvvk7p8S0rGs9atVfb80vV2I7ftWXxaKfgxlWC9rDL4mWxSDuPfbOJNSNMQOjz9Pvb4xyixsZG7d27V0888YQmTpyoXr16afjw4Xr22WeVkpKiZ555RgUFBT7pPAA0D4YkqaKmXpOf2e6yfUcCk+Z7ZLlK0N5VUO4UDEnS9vxvcpMAhDePc4h69eql6upqpaamavTo0XryySc1ZswY9e7d25/9A2BCuUdKWgRDdhU19fpXfqnT9FlBaZX2FJ5u9Rp3mu6RZU/Q9kSjzucmffTFGV11ca92/W4AocHjgOj3v/+9xowZ45TDAwD+kPfFmTbPH/i8Qt/OSnQ5tdWa7Iti9Omps273yHKXoO3KA28e0n/P+7bX1wEIHR5Pmf3kJz8hGAIQEN9yM9oy8JLzNcVcTW215rFbB2hEnwSnY672yHK347orh/9TyTJ+IMx5VakaAAJh1OVJio2OdDkFFhsdqW9nJXo1tTUyK1FXXdxLa+cM9tseWcfLq1l1BoQxj0eIACCQ3r77OsU2S5K2rzKTvJvaWnzDN6PbmQndNebypFaDl/ZMmUnOeUgAwg8jRABCUnp8tA7+eoL+lV+qA59XtKhD5M3UVnl1ncdt3X1ua7WMGB0CwhsjRABC2rezEnXvuL4tijJelthDI7MSFdHGJs123ozetPa5ERaLhl0Wr+v6OPfDVR4SgPDDCBGAsLV8eo7mrTvYai5Re0dvXH2uPfCxRkf6LQ8JQPC0u1K12VCpGggdzbfPKCyr1senbHpp53Gf7lXmr8CH7T+AwPH71h1mQ0AEBJ+rukPNg55QHr3xpP8AfMvnW3cAQLC5qju042iZ0/YZ7laRBZMn/QcQHAREAMKCve5QQ7NB7QbD0Lb80pAvjBju/Qc6OwIiAGHBXX2g5hu0hppw7z/Q2REQAQgL7uoDhXphxHDvP9DZERABCAtt1QcalBGr4+XVIT3t1Fb/R2YlhmTOE2AmBEQAwsby6TktNmiNubCrPjxRoR+s3qcxf9iqWav2yuZiD7RQ4Kr/FHYEQgPL7j3EsnsgNBSUVmlP4WlZJP31wBc6cOKMU6KyvRjj2jmDg9dJN0K5NADQ2Xj6/U2lagBhwVUNH1eartoK1WAjM4FACAg1TJkBCJrcIyX64z8/07/cBDmS6xo+bWHVFgBvMEIEIOBOlFdryjM7VNEk1yc2OlJv332d0uNbrsay1/DxBqu2AHiDESIAAdc8GJKkipp6TX5mu8v27mr4NMWqLQDtQUAEIKByj5S0CIbsKmrqXU6fuavh0xSrtgC0B1NmAAIq74szbZ4/8HmFvp2V6HTMXsNnx9EylyvKHr6lP6u2AHQII0QAAupbF/dq8/zAS2JdHm+ths+iCX0JhgB0GCNEAAJq1OVJio2OdDltFhsd2WJ0yM4aHam1cwY7avjERXfTsk2f6ZZndjjajMxK1PLpObJGR/qt//BOQWmVTpyuIWBFyKMwo4cozAj4zsnyGk1+ZrvHq8xcmbVqb6tTaKFclNEsXNWNImBFMHj6/U1A5CECIsD3/pVfqgOfV2jgJbGtjgy5UlBapbHLcls9v2XxaEYjgoyAFaGCStUAQt63sxK9CoTs3C3DP15eLcMwmKoJktbqRoVDFXGYFwERgLDjbhn+s1uOat/xCsd7pmoCy5OAlYAIoYZVZgDCjn0ZfoTF4nQ8wmJRbHSkDpw443R8x9EyzVt3MIA9NDd3AStVxBGKCIgA+E1BaZW2HClRYZnv9xVztQw/55Jeqqipd8pbkZynauDfPxep7YCVKuIIVUyZAfC5QKwwar4M/9L47jpeXq0frN7X6jVmn6oJ5Mqv5dNzNG/dQaffRRVxhDICIgA+52pnevu0la9XGGUmfJM07W7RrNmnagL55+IqYDVzMIrQx5QZAJ+yrzAKxrQVUzWtC9afS2ZCd425PMnUzx7hgYAIgE95ssLIn1rb4sPsUzXB/nMBQh1TZgB8yh8rjLzZ/oGpGtdY+QW0jYAIgE+525nem+CkI0nATXOL8M2fy/ajpWpsMmvWnj8XoDNiygyAz/lq2qqtJGB450xNneobGp2CIUkaclmc6acTASnEA6IlS5bIYrE4vVJSUhznDcPQkiVLlJaWpgsvvFCjR4/Wxx9/7PQZtbW1mjdvnhISEtS9e3dNnjxZX3zxRaBvBQhb7alZY5+2WvvDa/X/XZ+lJ757lX5w3aU6XVPn8ecFMzm7M5q/Lk97C087HesiqWuXLl4vufd3HSMgGEJ+yqx///56//33He8jIiIcPz/xxBN68skntWbNGvXt21ePPPKIrr/+eh05ckQ9e/aUJC1YsEDvvPOO1q9fr/j4eC1atEiTJk3S/v37nT4LgLOOTFe5utaVtj6P7R98p7W9xRolr/YWYwd7dGYhPUIkSV27dlVKSorjlZh4fiNIwzD09NNP65e//KWmTp2q7OxsvfTSS6qpqdFrr70mSbLZbFq1apWWLVum8ePHKycnR6+88ooOHTrkFGQBaKkj01WurnWlrc8jCdh3fLXCjClMdGYhHxDl5+crLS1NmZmZuv3221VQUCBJKiwsVHFxsSZMmOBoGxUVpVGjRmnnzp2SpP3796u+vt6pTVpamrKzsx1tWlNbW6vKykqnF2AWHZmuau1aV9r6PGoK+Y4vgkumMNHZhXRANGTIEK1du1bvvfeeXnzxRRUXF2v48OEqLy9XcXGxJCk5OdnpmuTkZMe54uJidevWTbGxsa22ac3SpUtltVodr/T0dB/eGRDaOjKi4O5abz6PmkK+4YvgkjpG6OxCOodo4sSJjp8HDBigYcOGqXfv3nrppZc0dOhQSZKl2V9wwzBaHGvOkzb333+/Fi5c6HhfWVlJUATT6MiIgrtrvfk8agr5Tkf3FmMKE51dSAdEzXXv3l0DBgxQfn6+pkyZIun8KFBqaqqjTUlJiWPUKCUlRXV1daqoqHAaJSopKdHw4cPb/F1RUVGKiory/U0AYaAjtYTiundTbHSkKmrq3f4eT2vgUFOo4zoaXPqyvhQQikJ6yqy52tpaffrpp0pNTVVmZqZSUlK0efNmx/m6ujrl5uY6gp1rrrlGkZGRTm2Kiop0+PBhtwERYHbtna6avy5PlefcB0Oefh58qyN7izGFic4spEeIFi9erJtvvlmXXHKJSkpK9Mgjj6iyslKzZ8+WxWLRggUL9NhjjykrK0tZWVl67LHHFB0drRkzZkiSrFar5syZo0WLFik+Pl5xcXFavHixBgwYoPHjxwf57oDQ1p4RhdaWd9u9PGewLo6NZvorTDGFic4spAOiL774QtOnT1dZWZkSExM1dOhQ7d69WxkZGZKk++67T+fOndPPfvYzVVRUaMiQIdq0aZOjBpEkPfXUU+rataumTZumc+fOady4cVqzZg01iAAPeTNd5S7x9utGg+mvIPNmX7jW8GeIzshiGB6sjYUqKytltVpls9kUExMT7O4AAdPWF2jzcwWlVRq7LLfVz9qyeDRfpEFCUUWYlaff3yE9QgQgeNr6AjVktHqOxNvQ1FZRxbVzBgepV0DoCKukagCB09YXaFvnSLwNPRRVBNxjhAhAC60lR9u/QF2xnztdU0fibYhhXzjAPQIiAC20p9q0nf3L1ayJt75IWvY1iioC7hEQAXBypqZOz3xwtN3Xm/XLNZSTlimqCLhHDhEAJ/PX5eng52dcnrPvfcWmqy2F+k7w5HYBbWOECICDu8KKAzN6Ob5AO7IvVmfjLueqsCz4OToUVQTaRkAEwMFd7tDPxvRxTP/w5fqNcEpaNmtuF+AOAREAB2+Tb/lyPY+kZSD8kUMEwMGefEt+kHd4bkD4IyAC4ITk2/bhuQHhjb3MPMReZjAb8oPah+cGhBb2MgPQIZ7kB/miCGEoFjLsCPKqgPBEQATAa74oQhjKhQwBmA85RAC85osihKFeyBCAuRAQAfCKL3ZOZ/d1AKGGgAiAVzwpQhiIzwAAXyKHCIATd0nOvihCSCFDAKGGgAiAJM+TnH2xczq7rwMINUyZAZDkXZKzL4oQUsgQQCihMKOHKMyIzqygtEpjl+W2en7L4tEuR218UYSQQoYA/InCjAA81t7d2n1RhNAMhQw7W/FJoDMiIALCmK++aIOZ5NyZgwWKTwLhg4AICEO+/qINRpKzGYKFtvKy1s4ZHKReAXCFpGogDPmjynOgk5w7e6Vqik8C4YURIiDM2L9om2v6RdueER1rdKTWzhkckCRnf91DKGlvXhaA4CAgAsKMv79o25Pk7G0ekBmCBYpPAuGFgAgIM6H0RdvePKBQugd/ofgkEF7IIQLCjP2LNsJicToeYbFoZFZiQL9o25sHFEr34E8UnwTCB4UZPURhRoQSW0295q072KEVWh1d7t7eYo52vriHcEHxSSB4KMwIdGJNE6B3F5TLImnIZfFtBhL2ACguOlLLNuV7FYi4Cp46mgfkjyRuT4I8X9U98uZzzFB8Egh3BERAmDpTU6eH/vax28DGVZ5Pc63VxmkrR8hXeUC+CBY8yWXyVd0jM9RPAsyIHCIgTHmav+OqXXOt1cZp63eEUh6QJ8/CV3WPOnv9JMCsCIiAMORp0b/W2rXmePk3AZEnvyMUkoY96aeviiRSbBHovJgyA8KQp/k77to113Say9PfEahijq3xpJ/ueFr3yAz1kwCzIiACQlhribsltq/avK7sbK0k9/V+7FzVxvEmRyiYScOe9NPdYlpP853MUD8JMCsCIiAEtZW4W15dq12F5W1ef8p2TtI39X62Hy1VYxsxgatprnApLOhpP31xL+HyTAB4jzpEHqIOEQJp1qq9Lr50pZgLI1VRU+/2+ue+P1A3ZqfqTE2dfvrKAe0qcA6grkzpqXvHZykqMqLNaa5wqRXkST99dS/h8kwAnOfp9zcBkYcIiBAo7goeemJkVqLWzhnsMrBq3s6TL/JwKSzoST99dS/h8kwAs6MwIxCmvE2EdmVbfqm2fVbSZu0he7ufvrpfr80d2ma7cCks6Ek/fXUv4fJMAHgmpJfdL126VNdee6169uyppKQkTZkyRUeOHHFqc+edd8pisTi9hg51/se9trZW8+bNU0JCgrp3767Jkyfriy++COStAB7zNBHanYMnz3jUbuexcq3f+zlLxgGYWkgHRLm5ubr77ru1e/dubd68WV9//bUmTJig6mrnf7hvvPFGFRUVOV7vvvuu0/kFCxbozTff1Pr167V9+3ZVVVVp0qRJamhoCOTtAB5preCht3LSe3nc9hcbDmnMH7Zq1qq9snmQowQAnU1Y5RCVlpYqKSlJubm5GjlypKTzI0RnzpzRW2+95fIam82mxMREvfzyy7rtttskSadOnVJ6erreffdd3XDDDR79bnKIEEiuEnd7REWourZB7v7C2lc8eZJD1Na1ANAZePr9HdIjRM3ZbDZJUlxcnNPxrVu3KikpSX379tXcuXNVUlLiOLd//37V19drwoQJjmNpaWnKzs7Wzp07W/1dtbW1qqysdHoBgWLf+PRvdw9Xdtr5v8BVLoKhYZfFa3jveKdjTZfQu6ok3RYqLgMwq7BJqjYMQwsXLtR1112n7Oxsx/GJEyfqe9/7njIyMlRYWKgHH3xQY8eO1f79+xUVFaXi4mJ169ZNsbGxTp+XnJys4uLiVn/f0qVL9fDDD/vtfgBPLNuUr0+Lzjod6yKpX1qMls8Y6EjqbW3Fkz2wWr/3c/1iwyGPfy8VlwGYTdgERPfcc48++ugjbd++3em4fRpMkrKzszVo0CBlZGTo73//u6ZOndrq5xmGIUsbORr333+/Fi5c6HhfWVmp9PT0DtwB4Ky1KtRNz7taJdYo6fAp5xFLdyueBmfGtXrOFSouAzCbsAiI5s2bp7ffflvbtm3TxRdf3Gbb1NRUZWRkKD8/X5KUkpKiuro6VVRUOI0SlZSUaPjw4a1+TlRUlKKionxzA0ATbVWhbloPyJf7ZrVWYbk5Ki4DMKuQziEyDEP33HOPNmzYoA8++ECZmZlurykvL9fJkyeVmpoqSbrmmmsUGRmpzZs3O9oUFRXp8OHDbQZEgL/MX5enHUfLnI7tOFqmeesOOh3z5b5ZBaVVmnbtxRqY0cvpeGyzgoyB3qkeAEJFSI8Q3X333Xrttdf0t7/9TT179nTk/FitVl144YWqqqrSkiVL9N3vflepqak6fvy4HnjgASUkJOjWW291tJ0zZ44WLVqk+Ph4xcXFafHixRowYIDGjx8fzNuDCbU2DdY0mdk+OuOLfbNcjUZdmxGrO4dfqn4XWZWZ0J2KywCgEB8hWrlypWw2m0aPHq3U1FTH6/XXX5ckRURE6NChQ7rlllvUt29fzZ49W3379tWuXbvUs2dPx+c89dRTmjJliqZNm6YRI0YoOjpa77zzjiIiIoJ1azApT6bBmnK1SsybURxXo1EHPj+j1z/8whH8ZCZ015jLkwiGAJhaWNUhCibqEKE9midOu9unbMvi0S4Dk/aM4rT3dwWbu2RzAPAGe5kBQdRW4vT5abBSNTT7X5HY6EjFRXdz+Xn2VWQFpVXacqTEo2DBl0nZgeBpsjkA+ENIT5kB4aqtxOnl03MUc2HLL3h7dWpXztTUadaqvRq7LFc/WL3Po202fJmUHQieJpsDgD8QEAE+Zk+cbr683Z44/e8vKlThIpBplH2X+lJtOVLiVC26PcFCa3uiRVgsGpmVGFKjQ+6eGZWzAfgbU2aAj7mbqnK3C/2sv+x1/DwoI1bfyU7xeGVac8un57TYEy0Ul9aH2/QegM6HgAjwMXdTVd7sQv/hiQp9eKKizTaf/MfWarBg37oj1JfWh9v0HoDOhykzoBl74nJ7p2lam6rqIik7LUbpcd1dnm+vNTuPu20T6kvrw2l6D0DnxLJ7D7HsvvPz5Sone4K0q6kuSRreO17n6r7WwZO2DvXZLlSX0HvD1TNjlRmAjvL0+5uAyEMERJ3frFV7W60KvXbO4DavdVU7p6C0SnsKT+sv2wt1rLRKjW38TRuQFqNDzTZs9cbqH1yrMZcntfv6UBLq03sAwgt1iAAveLOlRlOuRpWGXRYvi0Xaeazc499/6FSlrBdGquqrr9vcfLU1nSnHxl5zCQACiYAIpmYf2fnS9lWb7Vpb5TR/XZ62H3UOpHYVeB4INWU7V69rL43VvuNtJ1E3xe70AOAbBEQwJVcjO22xj8A0nRqrPFfn8fWemjrwYj3xX1fr41M2vbTzuFNwNLx3vAzDOeAKxSX0ABCOCIhgSq4KHbpiH4GJjY7UrFV7nQKgHlG+3xzYom+mjCZdleYyn4YcGwDwPQIimE5r+UKuxFzYVY9OyXYZQFXVNvi8b0Mui3d67yqfhhwbAPA96hDBdNxVRW7KVlOvH760z+W2Eu1x5/CMDn8GAMD3CIhgOu6qIjfVKCm/pMpnvzuhR1Sb54+Xs2cXAAQDARE6NVdVp1urihwI8W4Cos60fB4Awgk5ROiU3FWddrXpaSAMvSxeI7MSWy0ASW4QAAQHI0TolFwlQe84WqZ56w5K+mbT02szYgPyl6DpnlzLp+doRJ8Ep/MsnweA4GKECJ2Op1WnC0qrtM/NTvK+0jTgCZcd6AHATAiI0Om4W0VmrzrtzWozb9invx6+pX+bAQ/L5wEgdBAQIay42kS1OXeryOyJy96sNvOGfTTIGh1JwAMAYYKACGHBXZJ0U/ZVZO4Sl1tr116PTx2gIZfFEwQBQBgiqRphwVWS9PajpY4k6ebcJS7bl+MvvqFvi3btlWy9gGAIAMIUI0QIea0lSTca0rb8Un108oyuSu/ldM5V4rJhGPpXfun5TVObJFOPzErUyjsG6qevHmizH0/81wDd938PtXqeGkIAEL4IiBDy3CU/P/DmIf1peo4jt8gwDKefq2q/1n3/999OO8c3tS2/VPUNjRp2WbzTTvJNjcxK1LRBl+i//11MDSEA6IQIiBDy3CU/Hz5VqbHLcjv0O3YVlOvte0bo8X/8j3Yecw6Khl0W75hqc1XQkRpCABD+CIgQ0uyryrKSevh0TzFXPj5VqdfmDlVhWbX2FJTL0PnK0k1HfqghBACdEwERQpKrVWX+Zt/ZzJP6QNQQAoDOhYAIAeVJHSHJ9aoyfxtyWXxAfx8AIHQQEMHnXAU97uoINb3G+N8tNgJpeG/qBwGAmREQwWfaCnpa22z1rlf2KzKii9M12WkxAetz0z4CAMzLYhg+KNFrApWVlbJarbLZbIqJCewXdriYtWqvyyXpOZf00odtbKLaxXK+ppDjvaRGP/Tv2oxY3Tn8UvW7yCpJJEUDgAl4+v3NCBE6xD7VFWGxtLrDfFvBkOQcDEnfBEPNAyV7vR9JLQKvLpL6pcVo+YyBkr4Jdpr+3DzwIRACANgREMEr9gAoLjpSyzbl+zXXp19ajA7/p9Lxvmm9n+a1gK5rtq9Z02CHwAcA4A4BETzij2Xw7qbGlk93Hu1pGthQCwgA4EsERPCIP5bBX5eVqK8bG7Wn4HSbW2G0FuxQCwgA4CsERHCrtc1V2+PxqQOUbL3AMapjq6lnKwwAQNAREMGhaYJ0g2E4gpY9had99juGsBUGACAEERB1UgWlVdpTeFoWnQ9C7DvAR1gs+s+Zc47jmQnddaamTtOe26XPXOwVFhsdqYqaep/0KTY6kukvAEBIIiAKMk+3svD0+jM1dfrpKwe0q6Dc/cWSBqT11KFTZ1s976tgyP5ZhWXVBD4AgJBjqoDo2Wef1e9//3sVFRWpf//+evrpp/Xtb387KH1xt5VFe6//urHR42BIUpvBkDvZaTE6fKrSfcMmjpeHfkDU0SAVABB+TBMQvf7661qwYIGeffZZjRgxQs8//7wmTpyoTz75RJdccknA+9PaVhbz1h3U2jmD23X99vxSv1R4duXxqQM0ODNOY5flenWdvVhiKOpokAoACF9dgt2BQHnyySc1Z84c/ehHP9KVV16pp59+Wunp6Vq5cmXA+2JftdXQbNeUhv/d1LSwrLpd1wcqGJLO5x9dlthDI7MSFWGxuG0fYbFoZFZiSI+4tBWkAgA6N1MERHV1ddq/f78mTJjgdHzChAnauXOny2tqa2tVWVnp9PKVE6dr2jx/vLztgMjd9f7UPLBZPj3HsZ2G3bDL4jW8d7zTsVBfSt/RIBUAEN5MMWVWVlamhoYGJScnOx1PTk5WcXGxy2uWLl2qhx9+2C/9yYiLbvO8u2kld9f7UvNVZs0Dm7aWzYfTUnpPgtRQvwcAQPuZIiCyszSb2jEMo8Uxu/vvv18LFy50vK+srFR6erpP+mGfanK1M3zTCs3tuX5wZpwkeZxYfdVFMfqk6Ky+bnTeKPWhm/tp5OVJykzo7lFg42rZfDgtpe9okAoACG+mCIgSEhIUERHRYjSopKSkxaiRXVRUlKKiovzWp+XTczpUobmt663RkSosq9bugnJHvSHp/ChH1y4WfVHhXIdIkv7/D09qx7EyjeidoO8Ncg78wimwaa+OBqkAgPBmMYxmSROd1JAhQ3TNNdfo2WefdRzr16+fbrnlFi1dutTt9ZWVlbJarbLZbIqJifFZvzo6rRRO01KhztU2IqwyA4Dw5un3tylGiCRp4cKFmjlzpgYNGqRhw4bphRde0Oeff6677rorqP3q6OiLGUZvAoVtRADAvEwTEN12220qLy/Xb37zGxUVFSk7O1vvvvuuMjIygt01hBiCTAAwH9NMmXWUv6bMAACA/3j6/W2KOkQAAABtISACAACmR0AEAABMj4AIAACYHgERAAAwPQIiAABgegREAADA9AiIAACA6REQAQAA0zPN1h0dZS/oXVlZGeSeAAAAT9m/t91tzEFA5KGzZ89KktLT04PcEwAA4K2zZ8/KarW2ep69zDzU2NioU6dOqWfPnrJYLMHujluVlZVKT0/XyZMn2XvND3i+/sOz9S+er//wbP2rvc/XMAydPXtWaWlp6tKl9UwhRog81KVLF1188cXB7obXYmJi+IvpRzxf/+HZ+hfP1394tv7Vnufb1siQHUnVAADA9AiIAACA6REQdVJRUVF66KGHFBUVFeyudEo8X//h2foXz9d/eLb+5e/nS1I1AAAwPUaIAACA6REQAQAA0yMgAgAApkdABAAATI+AKMxt27ZNN998s9LS0mSxWPTWW285nTcMQ0uWLFFaWpouvPBCjR49Wh9//HFwOhtmli5dqmuvvVY9e/ZUUlKSpkyZoiNHjji14fm238qVK3XVVVc5iqwNGzZM//jHPxzneba+s3TpUlksFi1YsMBxjOfbfkuWLJHFYnF6paSkOM7zbDvmP//5j77//e8rPj5e0dHR+ta3vqX9+/c7zvvr+RIQhbnq6mpdffXVWrFihcvzTzzxhJ588kmtWLFC+/btU0pKiq6//nrH3mxoXW5uru6++27t3r1bmzdv1tdff60JEyaourra0Ybn234XX3yxHn/8cX344Yf68MMPNXbsWN1yyy2Of9h4tr6xb98+vfDCC7rqqqucjvN8O6Z///4qKipyvA4dOuQ4x7Ntv4qKCo0YMUKRkZH6xz/+oU8++UTLli1Tr169HG389nwNdBqSjDfffNPxvrGx0UhJSTEef/xxx7GvvvrKsFqtxnPPPReEHoa3kpISQ5KRm5trGAbP1x9iY2ONP//5zzxbHzl79qyRlZVlbN682Rg1apRx7733GobBf7sd9dBDDxlXX321y3M82475+c9/blx33XWtnvfn82WEqBMrLCxUcXGxJkyY4DgWFRWlUaNGaefOnUHsWXiy2WySpLi4OEk8X19qaGjQ+vXrVV1drWHDhvFsfeTuu+/WTTfdpPHjxzsd5/l2XH5+vtLS0pSZmanbb79dBQUFkni2HfX2229r0KBB+t73vqekpCTl5OToxRdfdJz35/MlIOrEiouLJUnJyclOx5OTkx3n4BnDMLRw4UJdd911ys7OlsTz9YVDhw6pR48eioqK0l133aU333xT/fr149n6wPr163XgwAEtXbq0xTmeb8cMGTJEa9eu1XvvvacXX3xRxcXFGj58uMrLy3m2HVRQUKCVK1cqKytL7733nu666y7Nnz9fa9euleTf/3bZ7d4ELBaL03vDMFocQ9vuueceffTRR9q+fXuLczzf9rv88suVl5enM2fO6K9//atmz56t3Nxcx3mebfucPHlS9957rzZt2qQLLrig1XY83/aZOHGi4+cBAwZo2LBh6t27t1566SUNHTpUEs+2vRobGzVo0CA99thjkqScnBx9/PHHWrlypWbNmuVo54/nywhRJ2Zf9dA8ai4pKWkRXaN18+bN09tvv60tW7bo4osvdhzn+XZct27d1KdPHw0aNEhLly7V1VdfrT/+8Y882w7av3+/SkpKdM0116hr167q2rWrcnNz9ac//Uldu3Z1PEOer290795dAwYMUH5+Pv/tdlBqaqr69evndOzKK6/U559/Lsm//+4SEHVimZmZSklJ0ebNmx3H6urqlJubq+HDhwexZ+HBMAzdc8892rBhgz744ANlZmY6nef5+p5hGKqtreXZdtC4ceN06NAh5eXlOV6DBg3SHXfcoby8PF122WU8Xx+qra3Vp59+qtTUVP7b7aARI0a0KG/y2WefKSMjQ5Kf/93tUEo2gu7s2bPGwYMHjYMHDxqSjCeffNI4ePCgceLECcMwDOPxxx83rFarsWHDBuPQoUPG9OnTjdTUVKOysjLIPQ99P/3pTw2r1Wps3brVKCoqcrxqamocbXi+7Xf//fcb27ZtMwoLC42PPvrIeOCBB4wuXboYmzZtMgyDZ+trTVeZGQbPtyMWLVpkbN261SgoKDB2795tTJo0yejZs6dx/PhxwzB4th2xd+9eo2vXrsajjz5q5OfnG6+++qoRHR1tvPLKK442/nq+BERhbsuWLYakFq/Zs2cbhnF+ieJDDz1kpKSkGFFRUcbIkSONQ4cOBbfTYcLVc5VkrF692tGG59t+P/zhD42MjAyjW7duRmJiojFu3DhHMGQYPFtfax4Q8Xzb77bbbjNSU1ONyMhIIy0tzZg6darx8ccfO87zbDvmnXfeMbKzs42oqCjjiiuuMF544QWn8/56vhbDMIyOjTEBAACEN3KIAACA6REQAQAA0yMgAgAApkdABAAATI+ACAAAmB4BEQAAMD0CIgAAYHoERAAAwPQIiADAS3feeaemTJkS7G4A8CECIgAhK9iBx/Hjx2WxWJSXl+f1tRs2bNANN9yghISEdn8GgMAhIAIAP6iurtaIESP0+OOPB7srADxAQAQgLH3yySf6zne+ox49eig5OVkzZ85UWVmZ4/zo0aM1f/583XfffYqLi1NKSoqWLFni9Bn/8z//o+uuu04XXHCB+vXrp/fff18Wi0VvvfWWJCkzM1OSlJOTI4vFotGjRztd/4c//EGpqamKj4/X3Xffrfr6ese5mTNn6te//rXGjx/vl/sH4FsERADCTlFRkUaNGqVvfetb+vDDD7Vx40Z9+eWXmjZtmlO7l156Sd27d9eePXv0xBNP6De/+Y02b94sSWpsbNSUKVMUHR2tPXv26IUXXtAvf/lLp+v37t0rSXr//fdVVFSkDRs2OM5t2bJFx44d05YtW/TSSy9pzZo1WrNmjX9vHIDfdA12BwDAWytXrtTAgQP12GOPOY795S9/UXp6uj777DP17dtXknTVVVfpoYcekiRlZWVpxYoV+uc//6nrr79emzZt0rFjx7R161alpKRIkh599FFdf/31js9MTEyUJMXHxzva2MXGxmrFihWKiIjQFVdcoZtuukn//Oc/NXfuXL/eOwD/ICACEHb279+vLVu2qEePHi3OHTt2zCkgaio1NVUlJSWSpCNHjig9Pd0p0Bk8eLDHfejfv78iIiKcPvvQoUNe3QeA0EFABCDsNDY26uabb9bvfve7FudSU1MdP0dGRjqds1gsamxslCQZhiGLxdLuPrT12QDCDwERgLAzcOBA/fWvf9Wll16qrl3b98/YFVdcoc8//1xffvmlkpOTJUn79u1zatOtWzdJUkNDQ8c6DCDkkVQNIKTZbDbl5eU5vX7yk5/o9OnTmj59uvbu3auCggJt2rRJP/zhDz0OXq6//nr17t1bs2fP1kcffaQdO3Y4kqrtI0dJSUm68MILHUnbNpvN436fPn1aeXl5+uSTTySdn6LLy8tTcXGxl08AQCAQEAEIaVu3blVOTo7T69e//rV27NihhoYG3XDDDcrOzta9994rq9WqLl08+2ctIiJCb731lqqqqnTttdfqRz/6kX71q19Jki644AJJUteuXfWnP/1Jzz//vNLS0nTLLbd43O+3335bOTk5uummmyRJt99+u3JycvTcc895+QQABILFMAwj2J0AgFCwY8cOXXfddTp69Kh69+4d7O4ACCACIgCm9eabb6pHjx7KysrS0aNHde+99yo2Nlbbt28PdtcABBhJ1QBM6+zZs7rvvvt08uRJJSQkaPz48Vq2bFmwuwUgCBghAgAApkdSNQAAMD0CIgAAYHoERAAAwPQIiAAAgOkREAEAANMjIAIAAKZHQAQAAEyPgAgAAJje/wO6ftj4PBQRxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 方式二：用df自带的plot去绘制\n",
    "for x in list_col:\n",
    "    df.plot(kind='scatter',x=x,y='Weight') #kind参数需要写，用这个df.plot方法，x参数：只需填列名就行，因为已经指定数据是df了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a802b3dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 结论：从散点图可以看出：上述这些图呈现了每种属性与鱼的重量的散点分布图，从图中可以看出从上述这些图可以看出，大部分属性可以直接分析单\n",
    "# 个属性的变化对鱼的重量预测结果的影响，即使用简单的多元线性回归算法对鱼的重量预测的效果可能会比较好，但是由于最后鱼的种类与与鱼的重量\n",
    "# 是非线性关系，后续需要采用相对复杂的回归模型进行预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00d911b2",
   "metadata": {},
   "source": [
    "6.查看相关性系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a3f563e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.176783</td>\n",
       "      <td>-0.101176</td>\n",
       "      <td>-0.118425</td>\n",
       "      <td>-0.209489</td>\n",
       "      <td>-0.696969</td>\n",
       "      <td>-0.315235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>-0.176783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.915712</td>\n",
       "      <td>0.918618</td>\n",
       "      <td>0.923044</td>\n",
       "      <td>0.724345</td>\n",
       "      <td>0.886507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length1</th>\n",
       "      <td>-0.101176</td>\n",
       "      <td>0.915712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999517</td>\n",
       "      <td>0.992031</td>\n",
       "      <td>0.625378</td>\n",
       "      <td>0.867050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length2</th>\n",
       "      <td>-0.118425</td>\n",
       "      <td>0.918618</td>\n",
       "      <td>0.999517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994103</td>\n",
       "      <td>0.640441</td>\n",
       "      <td>0.873547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length3</th>\n",
       "      <td>-0.209489</td>\n",
       "      <td>0.923044</td>\n",
       "      <td>0.992031</td>\n",
       "      <td>0.994103</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.703409</td>\n",
       "      <td>0.878520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>-0.696969</td>\n",
       "      <td>0.724345</td>\n",
       "      <td>0.625378</td>\n",
       "      <td>0.640441</td>\n",
       "      <td>0.703409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Width</th>\n",
       "      <td>-0.315235</td>\n",
       "      <td>0.886507</td>\n",
       "      <td>0.867050</td>\n",
       "      <td>0.873547</td>\n",
       "      <td>0.878520</td>\n",
       "      <td>0.792881</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Species    Weight   Length1   Length2   Length3    Height     Width\n",
       "Species  1.000000 -0.176783 -0.101176 -0.118425 -0.209489 -0.696969 -0.315235\n",
       "Weight  -0.176783  1.000000  0.915712  0.918618  0.923044  0.724345  0.886507\n",
       "Length1 -0.101176  0.915712  1.000000  0.999517  0.992031  0.625378  0.867050\n",
       "Length2 -0.118425  0.918618  0.999517  1.000000  0.994103  0.640441  0.873547\n",
       "Length3 -0.209489  0.923044  0.992031  0.994103  1.000000  0.703409  0.878520\n",
       "Height  -0.696969  0.724345  0.625378  0.640441  0.703409  1.000000  0.792881\n",
       "Width   -0.315235  0.886507  0.867050  0.873547  0.878520  0.792881  1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "cor = df.corr()   #默认皮尔逊相关性系数法\n",
    "cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "60ac9f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用热点图更容易看出\n",
    "sns.heatmap(data=cor,annot=True,cmap='Dark2')   #annot:是否在方块中显示数字   cmap：指定颜色组合\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "73f147b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_27944\\23227443.py:4: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  mask = np.zeros_like(cor,dtype=np.bool)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#其实在观察相关性系数的时候，因为从对角线分开，左下角和右上角两半部分的值是一样的，所以我们可以只看一半的图\n",
    "plt.figure(figsize=(16,8))\n",
    "#配置下三角热力图区域显示模式\n",
    "mask = np.zeros_like(cor,dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.set_style(style=\"white\")\n",
    "#对相关系数图进行下三角显示\n",
    "sns.heatmap(cor,annot=True,cmap=\"RdBu\",mask=mask)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "405545be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Temp\\ipykernel_27944\\1810330638.py:4: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  mask = np.zeros_like(cor[cor>=0.7],dtype=np.bool)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#只显示相关性系数>0.7的，也就是只显示强相关模式\n",
    "plt.figure(figsize=(16,8))\n",
    "#配置强相关模式，相关系数大于 0.7\n",
    "mask = np.zeros_like(cor[cor>=0.7],dtype=np.bool)\n",
    "# Create a msk to draw only lower diagonal corr map\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.set_style(style=\"white\")\n",
    "#显示强相关模式的相关系数热力值，低于参考值的部分显示为白色，从而获取强相关项\n",
    "sns.heatmap(cor[cor>=.7],annot=True,mask=mask,cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea9f4d31",
   "metadata": {},
   "source": [
    "相关性系数就是特征选择中的一种方法\n",
    "做特征选择目的：\n",
    "    1.剔除和目标列不相关的列\n",
    "    2.剔除冗余数据，也就是两个列之间的相关性系数比较高，那么可以删掉其中1列\n",
    "    3.可以在一定程度上起到降维的作用，但是效果有限"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9bbc9508",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从热力图的相关性系数来看，Length1、Length2和Length3的相关性系数是比较高的，所以存在冗余，可以考虑删除Length1，Length2两列，或者删除Length3这1列\n",
    "# 一次性删除两列数据可以减少建模时的数据量，提高建模的效率\n",
    "del df['Length1']\n",
    "del df['Length2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "179bd102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.880503</td>\n",
       "      <td>398.326415</td>\n",
       "      <td>31.227044</td>\n",
       "      <td>8.970994</td>\n",
       "      <td>4.417486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.026298</td>\n",
       "      <td>357.978317</td>\n",
       "      <td>11.610246</td>\n",
       "      <td>4.286208</td>\n",
       "      <td>1.685804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>1.728400</td>\n",
       "      <td>1.047600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>23.150000</td>\n",
       "      <td>5.944800</td>\n",
       "      <td>3.385650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>273.000000</td>\n",
       "      <td>29.400000</td>\n",
       "      <td>7.786000</td>\n",
       "      <td>4.248500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>12.365900</td>\n",
       "      <td>5.584500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>18.957000</td>\n",
       "      <td>8.142000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Species       Weight     Length3      Height       Width\n",
       "count  159.000000   159.000000  159.000000  159.000000  159.000000\n",
       "mean     2.880503   398.326415   31.227044    8.970994    4.417486\n",
       "std      2.026298   357.978317   11.610246    4.286208    1.685804\n",
       "min      0.000000     0.000000    8.800000    1.728400    1.047600\n",
       "25%      1.000000   120.000000   23.150000    5.944800    3.385650\n",
       "50%      4.000000   273.000000   29.400000    7.786000    4.248500\n",
       "75%      4.000000   650.000000   39.650000   12.365900    5.584500\n",
       "max      6.000000  1650.000000   68.000000   18.957000    8.142000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察数据看是否需要做特征缩放\n",
    "df.describe()   # 通过观察每列的最小值和最大值，发现Species列的取值范围[0,6],Weight的取值范围为[0,1650],两列的取值范围差距过大，需要做特征缩放"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be604a91",
   "metadata": {},
   "source": [
    "7.数据拆分  一般来说在做特征缩放时会将训练集和测试集分开进行特征缩放，这样做效果会好一些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "922ef073",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#纵向拆分\n",
    "# X = df.iloc[:,1:]   #获取非目标列也可以这么取  行列切片取值\n",
    "X = df.drop(axis=1,columns=['Weight'])   #获取非目标列\n",
    "Y = df['Weight']\n",
    "#横向拆分\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26be9310",
   "metadata": {},
   "source": [
    "8.特征缩放-标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ad344cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler   #特征缩放 \n",
    "\n",
    "#三步走\n",
    "#定义规则\n",
    "sc = StandardScaler()\n",
    "#将规则应用到数据上 ,训练集和测试集都得进行缩放\n",
    "X_train = sc.fit_transform(X=X_train)\n",
    "X_test = sc.fit_transform(X=X_test)\n",
    "# 类型转换(可选)，转成datafame类型后会加上列名、索引，方便操作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab0931c",
   "metadata": {},
   "source": [
    "9.使用线性回归进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "761bcaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#因为我们预测鱼的重量属于回归问题，所以我们得用回归模型\n",
    "from sklearn.linear_model import LinearRegression #线性回归的原理就是寻找多个自变量和因变量之间的关系\n",
    "\n",
    "model = LinearRegression()  # 设置参数\n",
    "model.fit(X=X_train,y=y_train)  #训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "16ba8f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8936408411178272"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=X_test,y=y_test)  #评分  准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d6f2b4",
   "metadata": {},
   "source": [
    "10.使用KNN算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "174a74ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(leaf_size=20, n_neighbors=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(leaf_size=20, n_neighbors=7)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor(leaf_size=20, n_neighbors=7)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "model = KNeighborsRegressor(n_neighbors=7,leaf_size=20)  # 设置参数\n",
    "model.fit(X_train,y_train)  # 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a1c8d360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9619629261383129"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)  # 准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e3ddea",
   "metadata": {},
   "source": [
    "11.使用决策树算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2c0774ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "model = DecisionTreeRegressor()  # 设置参数\n",
    "model.fit(X_train,y_train)  # 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cf4e1ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9459452274249366"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)  # 准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32aa8fe3",
   "metadata": {},
   "source": [
    "12.使用SVM算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "22ce1a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "model = SVR()  # 设置参数\n",
    "model.fit(X_train,y_train)  # 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cc240109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014728259675096833"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)  # 准确率  分数很低，说明SVM确实不适合用在回归场景中"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c296bb0",
   "metadata": {},
   "source": [
    "13.使用随机森林算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f7eade4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor()  # 设置参数\n",
    "model.fit(X_train,y_train)  # 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "69c47cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9682090035847477"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)  # 准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e2f15e",
   "metadata": {},
   "source": [
    "14.使用adaboost算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "798480a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostRegressor()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "\n",
    "model = AdaBoostRegressor()  # 设置参数\n",
    "model.fit(X_train,y_train)  # 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e537fc79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9496697436125263"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)  # 准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d115a3b",
   "metadata": {},
   "source": [
    "15.使用GBDT算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "17c0a024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "model = GradientBoostingRegressor()  # 设置参数\n",
    "model.fit(X_train,y_train)  # 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1ef9303d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9638383828195075"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)  # 准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34d93452",
   "metadata": {},
   "source": [
    "16.使用XGboost算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0f84835f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "model = XGBRegressor()  # 设置参数\n",
    "model.fit(X_train,y_train)  # 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b03d407c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9609453724493112"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)  # 准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12ab69ab",
   "metadata": {},
   "source": [
    "17.使用LightGBM算法进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "afc9c9ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000050 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 114\n",
      "[LightGBM] [Info] Number of data points in the train set: 106, number of used features: 4\n",
      "[LightGBM] [Info] Start training from score 401.970755\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightgbm import LGBMRegressor\n",
    "model = LGBMRegressor()  # 设置参数\n",
    "model.fit(X_train,y_train)  # 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d71f6ce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9503798596658297"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)  # 准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f4b34b8",
   "metadata": {},
   "source": [
    "18.使用多个('K最近邻','决策树','支持向量机','随机森林','自适应提升','梯度提升','xgboost')模型进行建模,并将模型的名字和评分存储在一个DataFrame中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "02fd2c66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K最近邻的准确率为: 0.9348610579434754\n",
      "决策树的准确率为: 0.9360399114053565\n",
      "支持向量机的准确率为: 0.014728259675096833\n",
      "随机森林的准确率为: 0.9688065882888403\n",
      "自适应提升的准确率为: 0.9520397163526442\n",
      "梯度提升的准确率为: 0.9644128084942045\n",
      "xgboost的准确率为: 0.9609453724493112\n"
     ]
    }
   ],
   "source": [
    "# 方式1：使用df的append方法来存储在DataFrame中\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "df_score1 = pd.DataFrame(columns=['model_name','model_score'])  # 定义这个是为了题目中想把模型和分数作为一个dataframe来输出\n",
    "model_knn = KNeighborsRegressor(n_neighbors=3)\n",
    "model_tree = DecisionTreeRegressor(max_depth=4)\n",
    "model_svm = SVR()\n",
    "model_rf = RandomForestRegressor(n_estimators=50)\n",
    "model_ada = AdaBoostRegressor()\n",
    "model_gbdt = GradientBoostingRegressor()\n",
    "model_xgb = XGBRegressor()\n",
    "# model_lgbm = LGBMRegressor()\n",
    "list_models=[model_knn,model_tree,model_svm,model_rf,model_ada,model_gbdt,model_xgb]\n",
    "list_model_names = ['K最近邻','决策树','支持向量机','随机森林','自适应提升','梯度提升','xgboost']\n",
    "row = 0\n",
    "for model,name in zip(list_models,list_model_names):\n",
    "    model.fit(X_train,y_train)  # 训练\n",
    "    model_score  = model.score(X_test,y_test)\n",
    "    #这里加index可以保证向df_score1中增加一行数据时,index不一样,如果不设置index,那么每加一行则index不会变\n",
    "    df_score2 = pd.DataFrame(data=[[name,model_score]],columns=['model_name','model_score'],index=[row])  \n",
    "    print(name+'的准确率为:',model_score)\n",
    "#     df_score1 = pd.concat((df_score1,df_score2)) #和下面一行是一样的  concat必须两个df列名相同\n",
    "    df_score1 = df_score1.append(df_score2)  #相当于给dataframe中追加一行  列名不相同时，相当于增加了1列\n",
    "    row =row+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c937e92c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>model_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K最近邻</td>\n",
       "      <td>0.934861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>决策树</td>\n",
       "      <td>0.936040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>支持向量机</td>\n",
       "      <td>0.014728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>随机森林</td>\n",
       "      <td>0.968807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>自适应提升</td>\n",
       "      <td>0.952040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>梯度提升</td>\n",
       "      <td>0.964413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>xgboost</td>\n",
       "      <td>0.960945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model_name  model_score\n",
       "0       K最近邻     0.934861\n",
       "1        决策树     0.936040\n",
       "2      支持向量机     0.014728\n",
       "3       随机森林     0.968807\n",
       "4      自适应提升     0.952040\n",
       "5       梯度提升     0.964413\n",
       "6    xgboost     0.960945"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "034507ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K最近邻的准确率为: 0.9348610579434754\n",
      "决策树的准确率为: 0.9360399114053565\n",
      "支持向量机的准确率为: 0.014728259675096833\n",
      "随机森林的准确率为: 0.9683937730579615\n",
      "自适应提升的准确率为: 0.9480330186842626\n",
      "梯度提升的准确率为: 0.962893292095678\n",
      "xgboost的准确率为: 0.9609453724493112\n"
     ]
    }
   ],
   "source": [
    "# 方式2：使用df的loc方法来存储在DataFrame中    如果实验题以填空题的方式出现,那么一般会以方式2的形式\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "df_score1 = pd.DataFrame(columns=['model_name','model_score']) # 定义个dataframe用于存储模型名和评分\n",
    "\n",
    "model_knn = KNeighborsRegressor(n_neighbors=3)\n",
    "model_tree = DecisionTreeRegressor(max_depth=4)\n",
    "model_svm = SVR()\n",
    "model_rf = RandomForestRegressor(n_estimators=50)\n",
    "model_ada = AdaBoostRegressor()\n",
    "model_gbdt = GradientBoostingRegressor()\n",
    "model_xgb = XGBRegressor()\n",
    "# model_lgbm = LGBMRegressor()\n",
    "list_models=[model_knn,model_tree,model_svm,model_rf,model_ada,model_gbdt,model_xgb]\n",
    "list_model_names = ['K最近邻','决策树','支持向量机','随机森林','自适应提升','梯度提升','xgboost']\n",
    "row = 0\n",
    "for model,name in zip(list_models,list_model_names):  #zip就是可以同时遍历多个列表\n",
    "    model.fit(X_train,y_train)  # 训练\n",
    "    model_score  = model.score(X_test,y_test)\n",
    "    print(name+'的准确率为:',model_score)\n",
    "    df_score1.loc[row,'model_name'] = name\n",
    "    df_score1.loc[row,'model_score'] = model_score\n",
    "    row =row+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f0199171",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>model_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K最近邻</td>\n",
       "      <td>0.934861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>决策树</td>\n",
       "      <td>0.93604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>支持向量机</td>\n",
       "      <td>0.014728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>随机森林</td>\n",
       "      <td>0.968394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>自适应提升</td>\n",
       "      <td>0.948033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>梯度提升</td>\n",
       "      <td>0.962893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>xgboost</td>\n",
       "      <td>0.960945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model_name model_score\n",
       "0       K最近邻    0.934861\n",
       "1        决策树     0.93604\n",
       "2      支持向量机    0.014728\n",
       "3       随机森林    0.968394\n",
       "4      自适应提升    0.948033\n",
       "5       梯度提升    0.962893\n",
       "6    xgboost    0.960945"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#得到一个关于模型名字和评分的DataFrame,里面有两列,1列名字,1列评分\n",
    "df_score1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3d10fe10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K最近邻的准确率为: 0.9348610579434754\n",
      "决策树的准确率为: 0.927205552660354\n",
      "支持向量机的准确率为: 0.014728259675096833\n",
      "随机森林的准确率为: 0.9656614928155136\n",
      "自适应提升的准确率为: 0.9496614559969989\n",
      "梯度提升的准确率为: 0.961827098009153\n",
      "xgboost的准确率为: 0.9609453724493112\n"
     ]
    }
   ],
   "source": [
    "# 方式3：自己优化的，使用df的loc方法来存储在DataFrame中    如果实验题以填空题的方式出现,那么一般会以方式2的形式\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "df_score1 = pd.DataFrame(columns=['model_name','model_score']) # 定义个dataframe用于存储模型名和评分\n",
    "\n",
    "model_knn = KNeighborsRegressor(n_neighbors=3)\n",
    "model_tree = DecisionTreeRegressor(max_depth=4)\n",
    "model_svm = SVR()\n",
    "model_rf = RandomForestRegressor(n_estimators=50)\n",
    "model_ada = AdaBoostRegressor()\n",
    "model_gbdt = GradientBoostingRegressor()\n",
    "model_xgb = XGBRegressor()\n",
    "# model_lgbm = LGBMRegressor()\n",
    "list_models=[model_knn,model_tree,model_svm,model_rf,model_ada,model_gbdt,model_xgb]\n",
    "list_model_names = ['K最近邻','决策树','支持向量机','随机森林','自适应提升','梯度提升','xgboost']\n",
    "row = 0\n",
    "for model,name in zip(list_models,list_model_names):  #zip就是可以同时遍历多个列表\n",
    "    model.fit(X_train,y_train)  # 训练\n",
    "    model_score  = model.score(X_test,y_test)\n",
    "    print(name+'的准确率为:',model_score)\n",
    "    df_score1.loc[row] = [name,model_score]\n",
    "    row =row+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f5051954",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>model_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K最近邻</td>\n",
       "      <td>0.934861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>决策树</td>\n",
       "      <td>0.927206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>支持向量机</td>\n",
       "      <td>0.014728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>随机森林</td>\n",
       "      <td>0.965661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>自适应提升</td>\n",
       "      <td>0.949661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>梯度提升</td>\n",
       "      <td>0.961827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>xgboost</td>\n",
       "      <td>0.960945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model_name  model_score\n",
       "0       K最近邻     0.934861\n",
       "1        决策树     0.927206\n",
       "2      支持向量机     0.014728\n",
       "3       随机森林     0.965661\n",
       "4      自适应提升     0.949661\n",
       "5       梯度提升     0.961827\n",
       "6    xgboost     0.960945"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#得到一个关于模型名字和评分的DataFrame,里面有两列,1列名字,1列评分\n",
    "df_score1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23bf1d6",
   "metadata": {},
   "source": [
    "19.模型优化-用网格搜索"
   ]
  },
  {
   "cell_type": "raw",
   "id": "32464c26",
   "metadata": {},
   "source": [
    "模型优化的作用：就是为了得到一个更可靠(评分)的模型。\n",
    "   模型的参数：就是每个算法(方法)都有属于自己的参数。参数在不同的场景下是不一样的需要我们进行配置。\n",
    "     自动调整参数的方法：网格搜索。其原理就是自动使用给定的参数进行建模并进行评分。最终通过评分来得到一个最优的模型\n",
    "   模型使用的数据：\n",
    "   更换不同的模型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ee87f34b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [3, 4, 5, 6, 7],\n",
       "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [3, 4, 5, 6, 7],\n",
       "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
       "             param_grid={'n_neighbors': [3, 4, 5, 6, 7],\n",
       "                         'weights': ['uniform', 'distance']})"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import  GridSearchCV   # 网格搜索\n",
    "\n",
    "model = KNeighborsRegressor()  # 需要调优的模型\n",
    "# 需要调整的参数和值 ,参数的值可以根据模型方法里面的参数提示去写:shift+tab\n",
    "parms = {'n_neighbors':[3,4,5,6,7],'weights':['uniform', 'distance']}  \n",
    "\n",
    "model_gs = GridSearchCV(estimator=model,param_grid=parms,cv=5)  # 网格搜索   \n",
    "# cv：表示进行交叉验证考试的时候不建议指定。但是使用的时候肯定要指定\n",
    "model_gs.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "88dd708e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(n_neighbors=4, weights=&#x27;distance&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(n_neighbors=4, weights=&#x27;distance&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=4, weights='distance')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gs.best_estimator_  # 最优的模型,得到的最优模型就可以对数据进行预测了\n",
    "# model_gs.best_estimator_.predict(X_test)   进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fffd8ee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9579707873673439"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gs.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1d533b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 4, 'weights': 'distance'}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gs.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9825c2f2",
   "metadata": {},
   "source": [
    "20.保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "67153e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3c79b245",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "76165f8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./fish_refrssor.pkl']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model_gs.best_estimator_,'./fish_refrssor.pkl')   # fish_refrssor.pkl没有后缀也行\n",
    "# 在'15综合大数实验手册预测鱼的重量.ipynb'文件中应用这个保存的模型去预测另外一份文件的数据,也相当于模型应用了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "969846e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.5200</td>\n",
       "      <td>4.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>12.4800</td>\n",
       "      <td>4.3056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>31.1</td>\n",
       "      <td>12.3778</td>\n",
       "      <td>4.6961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>12.7300</td>\n",
       "      <td>4.4555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>12.4440</td>\n",
       "      <td>5.1340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Species  Weight  Length3   Height   Width\n",
       "0        0   242.0     30.0  11.5200  4.0200\n",
       "1        0   290.0     31.2  12.4800  4.3056\n",
       "2        0   340.0     31.1  12.3778  4.6961\n",
       "3        0   363.0     33.5  12.7300  4.4555\n",
       "4        0   430.0     34.0  12.4440  5.1340"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edee7c33",
   "metadata": {},
   "source": [
    "21.模型评估"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ae13bbda",
   "metadata": {},
   "source": [
    "分类问题模型评估：这个是在2240120聚类算法03视频中才说到\n",
    "            评估角度：\n",
    "                偏差：指的是预测值和真实值之间的偏差程度\n",
    "                方差：指的是模型在相同大小的不同的数据集上的表现能力，这里一般会通过交叉验证来评估\n",
    "            好的模型：低偏差、低方差\n",
    "            过拟合：低偏差、高方差。重点\n",
    "            欠拟合：高偏差、低方差\n",
    "            分类问题偏差：从准确率、查全率、F1值、roc曲线下方面积(AUC值)去考虑，去分析偏差  在‘16森林覆盖类型分类预测.ipynb’\n",
    "            混淆矩阵：\n",
    "                P(正例/正元组)：感兴趣的类别\n",
    "                N(负例/负元组)：不感兴趣的类别\n",
    "                TP真正例：原本是正例也被预测为正例\n",
    "                TN真负例：原本为负例也被预测为负例\n",
    "                FP假正例：原本为负例被预测为正例\n",
    "                FN假负例：原本为正例被预测为负例\n",
    "                例：\n",
    "                    yes:是诈骗电话：准确率高一点，感兴趣 \n",
    "                    no:不是诈骗电话：准确率低一点，不感兴趣\n",
    "                    预测：\n",
    "                        原本为yes---->预测为yes        原本为yes---->预测为no\n",
    "                        原本为no----->预测为no         原本为no----->预测为yes\n",
    "                分类的偏差考察角度：\n",
    "                    准确率(精度)：(TP+TN)/P+N\n",
    "                    错误率(汉明损失)：(FN+FP)/P+N\n",
    "                    查准率：TP/(TP+FP),主要关注对负例的预测是否精准\n",
    "                    查全率：TP/(TP+FN),主要关注对正例的预测是否精准，也叫召回率\n",
    "                    F1值(常用)：F1值是查准率和查全率的一个调和平均值。其综合考虑了查准率和查全率\n",
    "                分类的方差考察角度：\n",
    "                    1.多做一些验证集\n",
    "                    2.交叉验证：k-fold交叉验证，k值一般指定为10，交叉验证中的评分就是偏差评估中的某一种\n",
    "        回归问题算法模型评估：\n",
    "            均方误差(MSE)：((80-81)²+(81-80)²+(82-81)²)/3，越小越好\n",
    "            均方根误差：均方误差开根号就行，在均方误差的基础之上开根号，越小越好\n",
    "            平均绝对误差：(|80-81|+|81-80|+|82-81|)/3，越小越好\n",
    "            R2:分子表示真实值与预测值的差的平方之和。分母是平方差，取值范围在0-1之间，越大越好\n",
    "                例：\n",
    "                    原来的值为：80、81、82\n",
    "                    预测的值为：81、80、81"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6879734b",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor()  # 设置参数\n",
    "model.fit(X_train,y_train)  # 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "804233b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#从偏差角度\n",
    "from sklearn.metrics import mean_absolute_error \n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0bce4fa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型的MAE为: 40.09487331536388\n",
      "模型的MAE为: 3489.9532892629954\n",
      "模型的MAE为: 0.9699481477496185\n"
     ]
    }
   ],
   "source": [
    "# 预测\n",
    "y_pre = model.predict(X_test)\n",
    "mae = mean_absolute_error(y_test,y_pre)  #MAE\n",
    "mse = mean_squared_error(y_test,y_pre)  #MSE\n",
    "r2_s = r2_score(y_test,y_pre)\n",
    "print('模型的MAE为:',mae)\n",
    "print('模型的MAE为:',mse)\n",
    "print('模型的MAE为:',r2_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f6de6574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.40273438,  0.58039508,  0.98067391,  0.96227478,  0.88421968,\n",
       "       -0.0391073 ,  0.92724191,  0.71547355,  0.64280897,  0.97091262])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import make_scorer \n",
    "from sklearn.model_selection import cross_val_score  #交叉验证的库  cross_val_score执行交叉验证并返回模型的性能评估结果  方差角度评估\n",
    "cv_s = cross_val_score(estimator=model,X=X,y=Y,cv=10,scoring=make_scorer(r2_score))\n",
    "cv_s   #从值可以看出，有一些评分过低，有一些高，那么就存在过拟合问题"
   ]
  }
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